import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
from long_tail_bench.samples.generalized_box_iou.pat_impl import (
    generalized_box_iou, )  # noqa
from long_tail_bench.samples.box_cxcywh_to_xyxy.pat_impl import (
    box_cxcywh_to_xyxy, )  # noqa
from long_tail_bench.core.executer import Executer


class HungarianMatcher(nn.Module):
    """This class computes an assignment between the targets and the
    predictions of the network

    For efficiency reasons, the targets don't include the no_object.
    Because of this, in general, there are more predictions than targets.
    In this case, we do a 1-to-1 matching of the best predictions,
    while the others are un-matched (and thus treated as non-objects).
    """

    def __init__(self,
                 cost_class: float = 1,
                 cost_bbox: float = 1,
                 cost_giou: float = 1):
        """Creates the matcher

        Params:
            cost_class: This is the relative weight of the classification
                error in the matching cost
            cost_bbox: This is the relative weight of the L1 error of the
                bounding box coordinates in the matching cost
            cost_giou: This is the relative weight of the giou loss of the
                bounding box in the matching cost
        """
        super().__init__()
        self.cost_class = cost_class
        self.cost_bbox = cost_bbox
        self.cost_giou = cost_giou
        assert (cost_class != 0 or cost_bbox != 0
                or cost_giou != 0), "all costs cant be 0"

    @torch.no_grad()
    def forward(self, outputs, targets):
        """Performs the matching

        Params:
            outputs: This is a dict that contains at least these entries:
                 "pred_logits": Tensor of dim [batch_size, num_queries,
                    num_classes] with the classification logits
                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4]
                    with the predicted box coordinates

            targets: This is a list of targets (len(targets) = batch_size),
                where each target is a dict containing:
                 "labels": Tensor of dim [num_target_boxes]
                    (where num_target_boxes is the number of ground-truth
                    objects in the target) containing the class labels
                 "boxes": Tensor of dim [num_target_boxes, 4] containing
                    the target box coordinates

        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j)
            where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets
                    (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries,
                    num_target_boxes)
        """
        bs, num_queries = outputs["pred_logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        out_prob = (outputs["pred_logits"].flatten(0, 1).softmax(-1)
                    )  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(
            0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        tgt_ids = torch.cat([v["labels"] for v in targets])
        tgt_bbox = torch.cat([v["boxes"] for v in targets])

        # Compute the classification cost. Contrary to the loss,
        # we don't use the NLL,
        # but approximate it in 1 - proba[target class].
        # The 1 is a constant that doesn't change the matching,
        # it can be ommitted.
        cost_class = -out_prob[:, tgt_ids]

        # Compute the L1 cost between boxes
        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)

        # Compute the giou cost betwen boxes
        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox),
                                         box_cxcywh_to_xyxy(tgt_bbox))

        # Final cost matrix
        C = (self.cost_bbox * cost_bbox + self.cost_class * cost_class +
             self.cost_giou * cost_giou)
        C = C.view(bs, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [
            linear_sum_assignment(c[i])
            for i, c in enumerate(C.split(sizes, -1))
        ]
        return [(
            torch.as_tensor(i, dtype=torch.int64),
            torch.as_tensor(j, dtype=torch.int64),
        ) for i, j in indices]


def args_adaptor(np_args):
    pred_logits = torch.from_numpy(np_args[0]).npu()
    pred_boxes = torch.from_numpy(np_args[1]).npu()
    outputs = {"pred_logits": pred_logits, "pred_boxes": pred_boxes}
    targets_list = np_args[2]
    targets = []
    for t in targets_list:
        labels = torch.from_numpy(t[0]).npu()
        boxes = torch.from_numpy(t[1]).npu()
        target = {"labels": labels, "boxes": boxes}
        targets.append(target)
    return [outputs, targets]


def executer_creator():
    coder_instance = HungarianMatcher(cost_class=1, cost_bbox=5, cost_giou=2)
    return Executer(coder_instance.forward, args_adaptor)
